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SMU SOE LKC Seminar in Econometrics (February 8, 2023): We Modeled Long Memory with Just One Lag!

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TOPIC:  

WE MODELED LONG MEMORY WITH JUST ONE LAG!

 

We build on two contributions that have found conditions for large dimensional networks or systems to generate long memory in their individual components, and provide a multivariate methodology for modeling and forecasting series displaying long range dependence. We model long memory properties within a vector autoregressive system of order 1 and consider Bayesian estimation or ridge regression. For these, we derive a theory-driven parametric setting that informs a prior distribution or a shrinkage target. Our proposal significantly outperforms univariate time-series long-memory models when forecasting a daily volatility measure for 250 U.S. company stocks over twelve years. This provides an empirical validation of the theoretical results showing long memory can be sourced to marginalization within a large dimensional system.
 
 
Keywords: Bayesian estimation, Ridge regression, Vector autoregressive model, Forecasting.
 
JEL Codes: C10, C32, C58.  
 
Click here to view the CV.
Click here to view the paper.
 
 

Guillaume Chevillon

ESSEC
 
Econometrics
Empirical Macroeconomics
Forecasting
 

8 February 2023 (Wednesday)

 

9.30am - 11am

 

Interactive Learning Room
School of Economics
Singapore Management University
90 Stamford Road
Singapore 178903